Bayesian Knowledge Tracing (BKT) has been in wide use for modeling student skill acquisition in Intelligent Tutoring Systems (ITS). Essentially being a Hidden Markov model (HMM), BKT tracks and updates student’s latent mastery of a skill as a probability distribution of a binary variable. BKT does so by accounting for observed student successes to apply the skill correctly, where success is also treated as a binary variable. While the BKT representation of acquiring skill mastery has been serving the ITS community well, representing both the latent state and the observed performance as binary variables are, nevertheless, a simplification. In addition, BKT as a two-state and two-observation first-order HMM is prone to noise in the data. In this paper, we present work that uses feature compensation and model compensation paradigms in an attempt to conceptualize a more flexible and robust Spectral BKT model. Validation of this model approach on the KDD Cup 2010 data shows a tangible boost in model accuracy well over the improvements recently seen in the literature.